Abstract: Most research on learning to identify sentiment ignores “neutral” examples, learning only from examples of significant (positive or negative) polarity. We show that it is crucial to use neutral examples in learning polarity for a variety of reasons. Learning from negative and positive examples alone will not permit accurate classification of neutral examples. Moreover, the use of neutral training examples in learning facilitates better distinction between positive and negative examples.

Abstract: Humor is one of the most interesting and puzzling aspects of human behavior. Despite the attention it has received in fields such as philosophy, linguistics, and psychology, there have been only few attempts to create computational models for humor recognition or generation. In this article, we bring empirical evidence that computational approaches can be successfully applied to the task of humor recognition. Through experiments performed on very large data sets, we show that automatic classification techniques can be effectively used to distinguish between humorous and non-humorous texts, with significant improvements observed over a priori known baselines.

Abstract: Computer systems keep growing in complexity, processing power and inter-connectivity. To leverage this rich environment and better assist users, a new type of intelligent assistant software is required. Building intelligent assistants is a difficult task that requires expertise in many AI related fields including natural language interpretation, dialog management, multimodal fusion and brokering of services. We believe that providing a unified tool and a set of associated methodologies to create intelligent software will bring many benefits to this area of research. Our solution, the Active framework, introduces the original concept of Active Ontologies to model and implement intelligent applications in a single and coherent software environment. As an example, this paper illustrates how Active has been used to implement an intelligent assistant to help surgeons in a computer equipped operating room.

Abstract: This paper proposes a rail track inspection technique using automated video analysis. This system is aimed to replace manual visual checks performed by the railway engineers for track inspection. We suggest a combination of image processing and analysis methods to achieve high performance automated rail track inspection. This paper addresses the problem of finding missing clips and finding blue clips which have been recently replaced in place of damaged clips. The experimental results show high performance in machine vision based inspection on a large sample of real train video.

Abstract: Gene expression profiles are composed of thousands of genes at the same time, representing the complex relationships between them. One of the well-known constraints specifically related to microarray data is the large number of genes in comparison with the small number of available experiments or cases. In this context, the ability of design methods capable of overcoming current limitations of state-of-the-art algorithms is crucial to the development of successful applications. This paper presents gene-CBR, a hybrid model that can perform cancer classification based on microarray data. The system employs a case-based reasoning model that incorporates a set of fuzzy prototypes, a growing cell structure network and a set of rules to provide an accurate diagnosis. The hybrid model has been implemented and tested with microarray data belonging to bone marrow cases from forty-three adult patients with cancer plus a group of six cases corresponding to healthy persons.

Abstract: The fundamental idea behind this encryption technique is the exploitation of DNA cryptographic strength, such as its storing capabilities and parallelism in order to enforce other conventional cryptographic algorithms. In this study, a binary form of data, such as plaintext messages, and images are transformed into sequences of DNA nucleotides. Subsequently, efficient searching algorithms are used to locate the multiple positions of a sequence of four DNA nucleotides. These four DNA nucleotides represent the binary octet of a single plaintext character or the single pixel of an image within, say, a Canis Familiaris genomic chromosome. The process of recording the locations of a sequence of four DNA nucleotides representing a single plain-text character, then returning a single randomly chosen position, will enable us to assemble a file of random pointers of the locations of the four DNA nucleotides in the searched Canis Families genome. We call the file containing the randomly selected position in the searchable DNA strand for each plain text character, the ciphered text. Since there is negligible correlation between the pointers file obtained from the selected genome, with its inherently massive storing capabilities, and the plain-text characters, the method, we believe, is robust against any type of cipher attacks.

Abstract: KASIMIR is a case-based decision support system in the domain of breast cancer treatment. For this system, a problem is given by the description of a patient and a solution is a set of therapeutic decisions. Given a target problem, KASIMIR provides several suggestions of solutions, based on several justified adaptations of source cases. Such adaptation processes are based on adaptation knowledge. The acquisition of this kind of knowledge from experts is presented in this paper. It is shown how the decomposition of adaptation processes by introduction of intermediate problems can highlight simple and generalizable adaptation steps. Moreover, some adaptation knowledge units that are generalized from the ones acquired for KASIMIR are presented. This knowledge can be instantiated in other case- based decision support systems, in particular in medicine.

Abstract: Time-varying information embedded in cases has often been neglected and its role oversimplified in case-based reasoning systems. In several real-world problems, and in particular in medical applications, a case should capture the evolution of the observed phenomenon over time. To this end, we propose to represent temporal information at two levels: (1) at the case level, when some features are collected in the form of time series, because they describe parameters varying within a period of time (which corresponds to the case duration), and we aim at analyzing the system behavior within the case duration interval itself; (2) at the history level, when we are interested in reconstructing the evolution of the system by retrieving temporally related cases. In this paper, we describe a framework for case representation and retrieval that is able to take into account the temporal dimension, and is meant to be used in any time dependent domain, which is particularly well suited for medical applications. To support case retrieval, we provide an analysis of similarity-based time series retrieval techniques; to support history retrieval, we introduce possible ways to summarize the case content, together with the corresponding strategies for identifying similar instances in the knowledge base. A concrete application of our framework is represented by RHENE, a system for intelligent retrieval in the hemodialysis domain.

Abstract: Quantum-behaved Particle Swarm Optimization (QPSO) is a global convergence guaranteed search method, which introduced quantum theory into original Particle Swarm Optimization (PSO) While Simulated Annealing (SA) is another important stochastic optimization with the ability of probabilistic hill-climbing In this paper, the mechanism of Simulated Annealing is introduced into the weak selection implicit in our QPSO algorithm, which effectively employs both the ability to jump out of the local minima in Simulated Annealing and the capacity of searching the global optimum in QPSO algorithm The experimental results show that the proposed hybrid algorithm increases the diversity of the population in the search process and improves its precision in the latter period of the search

Abstract: Case-based object recognition requires a general case of the object that should be detected. Real-world applications such as the recognition of biological objects in images cannot be solved by one general case. A case base is necessary to handle the great natural variations in the appearance of these objects. In this paper, we will present how to learn a hierarchical case base of general cases. We present our conceptual clustering algorithm to learn groups of similar cases from a set of acquired structural cases of fungal spores. Due to its concept description, it explicitly supplies for each cluster a generalized case and a measure for the degree of its generalization. The resulting hierarchical case base is used for applications in the field of case-based object recognition. We present results based on our application for health monitoring of biologically hazardous material.

Abstract: The research presented here explores the hypothesis that the deployment and acceptance of decision support systems in medicine will be enhanced if the basis for the recommendation produced by the system is apparent. We describe a decision support system for advising on patients suffering from bronchiolitis. This system supports its recommendations with precedent cases selected to support the recommendation along with justification text that highlights aspects of these cases relevant to the query case. It also presents an estimate of its confidence in the recommendation. The main contribution of this paper is an evaluation of this system in a clinical context. The evaluation shows that this type of explanation does enhance the usefulness of the system for practitioners.

Abstract: Trust and reputation systems have proven to be essential to enforcing cooperative behavior in peer-to-peer networks. We briefly describe the current approaches to building reputation systems: social networks formation, probabilistic estimation and game theoretic models. We then observe that all of the current models make a number of simplifying assumptions that may not necessarily hold in real networks, such as either irrational (probabilistic) or completely rational behavior, instant propagation of reputation information and homogeneity of interactions. We argue that dropping those assumptions and allowing more degrees of freedom is necessary in order to construct more realistic and rich reputation models. We support our argument by citing reputation research done in economics, evolutionary psychology, biology and sociology and and consider models that take into account adaptive behavior changes, co-evolution of behaviors, bounded rationality and variable interaction patterns. We then outline how those complexities can be dealt with and point out main directions for the future study of more realistic and less constrained reputation models that can potentially lead to construction of more secure, responsive and cooperative peer-to-peer systems.

Abstract: The standard particle swarm optimization (PSO) algorithm, existing improvements and their influence to the performance of standard PSO are introduced. The framework of PSO basic formula is analyzed. Implied by its three-term structure, the inherent shortcoming that trends to local optima is indicated. Then a modified velocity updating formula of particle swarm optimization algorithm is declared. The addition of the disturbance term based on existing structure effectively mends the defects. The convergence of the improved algorithm is analyzed. Simulation results demonstrated that the improved algorithm have a better performance than the standard one.

Abstract: Research on the optimization of Vehicle Routing Problem with Time Windows (VRPTW) is a significant investigation area of ant colony system (ACS). This paper proposes an enhanced ACS, which embeds the sequential insertion heuristic method, to solve VRPTW. The main idea is to organize two respective ant colonies to successively achieve a multiple objective minimization. Experiments on a series of benchmark problems demonstrate the excellent performance of ACS when compared with other optimization methods.

Abstract: This paper proposes a way of preparing and recording a speech corpus for unit selection text-to-speech speech synthesis driven by symbolic prosody. The research is focused on a phonetically and prosodically rich sentence selection algorithm. Symbolic description on a deep prosody level is used to enrich the phonetic representation of sentences (by respecting the prosodeme types phones appear in). The resulting algorithm then selects sentences with respect to both phonetic and prosodic criteria. To cover supra-sentential prosody phenomena, paragraphs were selected at random and recorded as well. The new speech corpus can be utilised in unit selection speech synthesis and also for training a data-driven prosodic parser.

Abstract: With the development of mirocarray technology, microarray data are widely used in the diagnoses of cancer subtypes. However, people are still facing the complicated problem of accurate diagnosis of cancer subtypes. Building classifiers based on the selected key genes from microarray data is a promising approach for the development of microarray technology; yet the selection of non-redundant but relevant genes is complicated. The selected genes should be small enough to allow diagnosis even in regular laboratories and ideally identify genes involved in cancer-specific regulatory pathways. Instead of the traditional gene selection methods used for the classification of two categories of cancers, in the present paper, a novel gene selection algorithm based on mutual information is proposed for the classification of multi-class cancer using microarray data, and the selected key genes are fed into the classifier to classify the cancer subtypes. In our algorithm, mutual information is employed to select key genes related with class distinction. The application on the breast cancer data suggests that the present algorithm can identify the key genes to the BRCA1 mutations/BRCA2 mutations/the sporadic mutations class distinction since the result of our proposed algorithm is promising, because our method can perform the classification of the three types of breast cancer effectively and efficiently. And two more microarray datasets, leukemia and ovarian cancer data, are also employed to validate the performance of our method. The performances of these applications demonstrate the high quality of our method. Based on the present work, our method can be widely used to discriminate different cancer subtypes, which will contribute to the development of technology for the recovery of the cancer.

Abstract: Face recognition using labeled and unlabelled data has received considerable amount of interest in the past years. In the same time, multiple classifier systems (MCS) have been widely successful in various pattern recognition applications such as face recognition. MCS have been very recently investigated in the context of semi-supervised learning. Very few attention has been devoted to verifying the usefulness of the newly developed semi-supervised MCS models for face recognition. In this work we attempt to access and compare the performance of several semi-supervised MCS training algorithms when applied to the face recognition problem. Experiments on a data set of face images are presented. Our experiments use nonhomogenous classifier ensemble, majority voting rule and compare between a three semi-supervised learning models: the self-trained single classifier model, the ensemble driven model and a newly proposed modified co-training model. Experimental results reveal that the investigated semi-supervised models are successful in the exploitation of unlabelled data to enhance the classifier performance and their combined output. The proposed semi-supervised learning model has shown a significant improvement of the classification accuracy compared to existing models.

Abstract: In this paper, the application of multiple Elman neural networks to time series data regression problems is studied. An ensemble of Elman networks is formed by boosting to enhance the performance of the individual networks. A modified version of the AdaBoost algorithm is employed to integrate the predictions from multiple networks. Two benchmark time series data sets, i.e., the Sunspot and Box-Jenkins gas furnace problems, are used to assess the effectiveness of the proposed system. The simulation results reveal that an ensemble of boosted Elman networks can achieve a higher degree of generalization as well as performance than that of the individual networks. The results are compared with those from other learning systems, and implications of the performance are discussed.

Abstract: Automatic face recognition has a lot of application areas and current single-camera face recognition has severe limitations when the subject is not cooperative, or there are pose changes and different illumination conditions. A face recognition system using multiple cameras overcomes these limitations. In each channel, real-time component-based face detection detects the face with moderate pose and illumination changes employing fusion of individual component detectors for eyes and mouth, and the normalized face is recognized using an LDA recognizer. A reliability measure is trained using the features extracted from both face detection and recognition processes, to evaluate the inherent quality of channel recognition. The recognition from the most reliable channel is selected as the final recognition results. The recognition rate is far better than that of either single channel, and consistently better than common classifier fusion rules

Abstract: Memoire proposes a general framework for reasoning from cases in biology and medicine. Part of this project is to propose a memory organization capable of handling large cases and case bases as occur in biomedical domains. This article presents the essential principles for an efficient memory organization based on pertinent work in information retrieval (IR). IR systems have been able to scale up to terabytes of data taking advantage of large databases research to build Internet search engines. They search for pertinent documents to answer a query using term-based ranking and/or global ranking schemes. Similarly, case-based reasoning (CBR) systems search for pertinent cases using a scoring function for ranking the cases. Memoire proposes a memory organization based on inverted indexes which may be powered by databases to search and rank efficiently through large case bases. It can be seen as a first step toward large-scale CBR systems, and in addition provides a framework for tight cooperation between CBR and IR.

Abstract: Based on the research of optimal foraging theory (OFT), we present a novel particle swarm optimizer (PSO) to improve the performance of standard PSO (SPSO). The resulting algorithm is known as PSOOFT that makes use of two mechanisms of OFT: a reproduction strategy to enhance the ability to converge rapidly to good solutions and a patch-choice based scheme to keep a right balance of exploration and exploitation. In the simulation studies, several benchmark functions are performed, and the performance of the proposed algorithm is compared to the standard PSO (SPSO). The experimental results show that the PSOOFT prevents premature convergence to a high degree, but still has a more rapid convergence rate than SPSO.

Abstract: The negotiation in general sense, as one of the most fundamental and powerful interaction of human beings, represents the dynamic process of exchanging information and perspectives towards mutual understanding and agreements. Interest based negotiation allows negotiators to discuss the concerns behind the negotiation issues so that a mutually acceptable win-win solution is more likely to be reached. This paper, for the first time, proposes a computational model for interest based negotiation automation which enables the automation of the fundamental elements of negotiation. Based on the model, algorithms are designated to automate the fundamental elements with practical computational complexity. This model provides not only a theoretical foundation for software agent based negotiation automation, but also a practical approach.

Abstract: Several proposals have been formulated to combine cryptography and biometrics in order to secure data and to strengthen the personal authentication process, making the falsification of personal ID, like passports, more challenging. In this work we describe a system for personal identification and verification, based on the combination of multiple biometric readings and other user inputs. In such way authentication control can be performed and only allowed persons can access the resources which must be protected. The method consists of two main phases: enrollment and verification. In the first phase the data extracted from user inputs are processed by the proposed system and stored the in a innovative non-reversible form. In the second phase, the stored data are combined with the biometric readings and other user inputs in order to identify and verify the identity of the person. The system does not rely on network access or databases to perform the verification/authentication phase.

Abstract: In standard particle swarm optimization, velocity information only provides a moving direction of each particle of the swarm, though it also can be considered as one point if there is no limitation restriction. Predicted-velocity particle swarm optimization is a new modified version using velocity and position to search the domain space equality. In some cases, velocity information may be effectively, but fails in others. This paper presents a game-theoretic approach for designing particle swarm optimization with a mixed strategy. The approach is applied to design a mixed strategy using velocity and position vectors. The experimental results show the mixed strategy can obtain the better performance than the best of pure strategy.

Abstract: There has been an explosion of interest in health sciences applications of case-based reasoning (CBR), not only in the traditional CBR in medicine domain, but also in bioinformatics, enabling home health-care technologies, CBR integration, and synergies between CBR and knowledge discovery. This special issue features the best papers from the third workshop on CBR in the health sciences, held at ICCBR-05 in Madrid. It is the third in a series of exciting workshops, the first two of which were held at ICCBR-03, in Trondheim, Norway, and at ECCBR-04, in Madrid, Spain. The nine high-quality papers introduced here represent the research and experience of twenty-two authors working in eight different countries on a wide range of problems and projects. These papers illustrate some of the major trends of current research in CBR in the health sciences, and represent overall an excellent sample of the most recent advances of CBR in the health sciences.

Abstract: In this paper, we propose a simple genetic algorithm for finding the optimal conformation of a protein using the three-dimensional square HP model. A backtracking procedure is used to resolve the positional collisions and illegal conformations that occur during the course of genetic search. Backtracking is shown to be a simple and efficient means of collision repair that requires little overhead. Empirical results show that a genetic algorithm using backtracking can obtain the lowest energy structure of an amino acid sequence in fewer energy evaluations than earlier approaches.

Abstract: Swarm-diversity is an important factor influencing the global convergence of particle swarm optimization (PSO) In order to overcome the premature convergence, the paper introduced a negative feedback mechanism into particle swarm optimization and developed an adaptive PSO The improved method takes advantage of the swarm-diversity to control the tuning of the inertia weight (PSO-DCIW), which in turn can adjust the swarm-diversity adaptively and contribute to a successful global search The proposed PSO-DCIW was applied to some well-known benchmarks and compared with the other notable improved methods for PSO The relative experimental results show PSO-DCIW is a robust global optimization method for the complex multimodal functions, which can improve the performance of the standard PSO and alleviate the premature convergence validly

Abstract: Purpose: To evaluate the usefulness of arthroscopic decompression and miniopen repair that was related with large and massive sized full thickness rotator cuff tear and assess clinical result. Materials and Methods: Twenthy-nine cases of miniopen repaired full thickness tear of rotator cuffs that arthroscopically decompressed were studied. From October 1998 to December 2004 we have analysed 29 repairs of large and massive sized FTRCT, the average age 44 () years old, mean follow-up was 34 () months. We analyzed the results statistically by paired t-test. Results: Postoperative VAS of pain improved average 7.0 to 1.7, UCLA score improved 13.7 to 31.9, ADL improved 11.3 to 25.3 respectively (all, P